Mapping Quantitative Traits
Preliminaries
If you are not already familiar with the structure of these exercises, read the Introduction first.
If you have not already worked through the first part of this exercise: Quantitative Traits, begin with that first. That page has the background and information you need to fully understand this case study.
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Contact information
If you have questions about these exercises, please contact Dr. Kevin Middleton (middletonk@missouri.edu) or drop by Tucker 224.
Learning objectives
The learning objectives for this exercise are:
- Describe what quantitative trait loci (QTL) are and outline QTL are identified
- Explain how the contributions of many genes of small effect can be associated with a disease or condition
- Differentiate Mendelian traits from threshold traits
- Compare Mendelian human diseases and diseases that result from threshold traits
Introduction
In the previous exercise (Quantitative Traits), we saw how we could build up a picture of quantitative, polygenic traits from your existing understanding of Mendelian traits. Furthermore, we saw how many many genes of small effect, each of which added or subtracted a small amount to a phenotype, can produce a continuous (normal) distribution of trait values.
To this point, we have only considered how genes contribute to a quantitative trait and how many genes might contribute to a traits. What we haven’t considered yet is how scientists estimate where in the genome the associated genes are located.
Genetic Variation
Ultimately, different phenotypes – both discrete qualitative phenotypes like blood types and quantitative like heights – result from genetic variation. Many different processes lead to variation, including mutation, drift, and selection among others.
Single Nucleotide Polymorphisms
Although many methods can be used to determine the locations of traits on the genome – “mapping” – one of the most common methods in the genomic era is via single nucleotide polymorphisms (SNPs). As their name suggests, SNPs are alternate nucleotides (e.g., an A or a T) at a single location in the genome. SNPs can occur in both coding and non-coding regions of the genome (Figure 1).
Because most of the genome is identical within a species, SNPs represent a relatively small percentage of the whole genome. For example, in humans, the entire genome consists of over 6 billion base pairs, but a recent study only used 2.3 million SNPs (Yengo et al. 2018). While 2.3 million may seem like a very large number, that represents only 0.04% of the genome.
Associating SNPs with traits
Shapiro pigeon example (dominant trait)
Human Mendelian diseases are “easy” to identify
Associating QTLs with genetic variants
Case study: Investigating a newly discovered muscle mutation in mice
GWAS
Primer (Uffelmann et al. 2021)
QTL for Human Height
Estimated to be ~700 explaining ~16% of variation in 2010 (Lango Allen et al. 2010)
Best understood quantitative trait in humans
Yet still 700 genes
Largest GWAS to date involves ~700,000 individuals described by Yengo et al. (2018)
3,290 (“near-independent”) SNPs explain ~25% of the phenotypic variation in human height among a sample of Europeans
Case Study: Threshold traits
Alzheimer (Pedersen et al. 2001)
Cardiac conditions (Walsh et al. 2020)
ASD (Grove et al. 2019)
Summary, Complex disease traits (Pal et al. 2015; Huang 2015)
Schizophrenia (~200 genes)
Why family history is one of the most important diagnostic tools in medicine
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